Humanoid robots, for example, robots having human characteristics, represent a major step in applying autonomous machine technology toward assisting persons in the home or office. Potential applications encompass a myriad of daily activities, such as attending infants and responding to queries and calls for assistance. Indoor humanoid robots may be expected to perform common household chores, such as making coffee, washing clothes, and cleaning a spill. Additional applications may include assisting elderly and handicapped persons. Humanoid robots will be expected to perform such tasks so as to satisfy the perceived desires and requests of their users. Through visual and voice recognition techniques, robots may be able to recognize and greet their users by name. In addition, robots should be able to learn through human interaction and other methods. In order to meet these needs, such robots should possess the requisite knowledge and reasoning ability.
In particular, to accomplish an indoor task, an autonomous system such as a humanoid robot should have a plan. It is desirable that such plans be fundamentally based on “common sense” knowledge. For example, steps for executing common household or office tasks can be collected from non-expert human volunteers using distributed capture techniques. However, it is also desirable that the robot be able to derive or update such a plan dynamically, taking into consideration aspects of the actual indoor environment where the task is to be performed. For example, the robot should be aware of the locations of objects involved in the plan. Furthermore, the robot should know how to respond to situations that arise before or during execution of the plan, such as a baby crying. Also, the robot should have awareness of the consequences of actions, such as the sink filling when the tap is on. In other words, the robot should be able to augment rules about performing tasks with knowledge of the actual environment, and with reasoning skills to adapt the rules to the environment in an appropriate manner. This integrated ability is referred to as commonsense reasoning.
Considerations for choosing a reasoning paradigm include the knowledge acquisition method, knowledge representation and scalability. Conventional approaches based upon knowledge and rules have been tailored to the specific knowledge used. Such representations are difficult to build and maintain. As the knowledge set is revised, iterative rule refinement is necessary to avoid interference among generated inferences. This may involve detailed design work that consumes increasing time and effort as the knowledge base grows.
Developments in reasoning in conjunction with knowledge bases have taken form in a number of conventional languages and systems. For example, pattern-recognition algorithms have been instantiated to search for facts, and traditional logic programming languages such as Prolog have utilized backward chaining. A description of this can be found in A. Colmerauer, An Introduction to Prolog III, Communications of the ACM 33(7):69-90, 1990, which is incorporated by reference herein in its entirety. Other reasoning systems based upon C Language Integrated Production System (CLIPS) have favored forward chaining. A description of this can be found in J. Giarratono and G. Riley, Expert Systems Principles and Programming, Third Edition, PWS Publishing Company, Boston, Mass., 1998, which is incorporated by reference herein in its entirety.
Use of a situational calculus within GOLOG or a fluent calculus within FLUX facilitate planning in dynamic environments. Descriptions of these concepts can be respectively found in H. Levesque, et al., Golog: A Logic Programming Language for Dynamic Domains, Journal of Logic Programming 31:59-84 1997, and in M. Thielscher, Programming of Reasoning and Planning Agents with Flux, Proc. of the 8th Int. Conf. on Principles of Knowledge Representation and Reasoning (KR2002), 263-315, Morgan Kaufmann Publishers, 2002, both of which are incorporated by reference herein in their entirety.
Systems built upon semantic networks such as SNePS have provided powerful search techniques on semi-structured knowledge to support belief revision and natural language processing. A description of this can be found in S.C. Shapiro and W. J. Rapaport, SNePS Considered as a Fully Intensional Propositional Semantic Network, Springer-Verlag, 263-315, New York, 1987, which is incorporated by reference herein in its entirety. Finally, approaches such as memory chunking in SOAR have provided methods to implement reflective introspection. A description of this can be found in P. S. Rosenbloom and J. E. Laird, Integrating Execution, Planning, And Learning in SOAR for External Environments, Proceedings of the Eighth National Conference on Artificial Intelligence, 1022-1029, MIT Press, Boston, Mass., 1990, which is incorporated by reference herein in its entirety.
For commonsense reasoning, a variety of knowledge sources and representations have been proposed for implementation. For example, knowledge bases such as CYC have been manually developed. A corresponding rule base utilizes heuristics and is also manually designed. A description of this can be found in R. V. Guha et al., CYC: A Midterm Report, Communications of the ACM 33(8): 391-407, 1990, which is incorporated by reference herein in its entirety. The OpenMind projects capture information provided by the public in natural-language form. Such knowledge capture is desirable for systems that interact with humans on a daily basis, but has proven difficult to use with logic-based reasoning. A description of this can be found in D. G. Stork, The OpenMind Initiative, IEEE Expert Systems and Their Applications 14(3):19-20, 1999, and in D. G. Stork, Open Data Collection for Training Intelligent Software in the Open Mind Initiative, Proceedings of the Engineering Intelligent Systems (EIS2000), 1999, both of which are incorporated by reference herein in their entirety.
From the above, there is a need for a practical method and system to enable an autonomous machine to perform indoor tasks and interact with humans through the integration of knowledge, rules and reasoning.